The Ministry of Health and Family Welfare said India had shared its concerns over the methodology with other member states through a series of official communications, including six letters to the WHO.

“India has shared its concerns over the methodology with other Member States through a series of official communications, including six letters to WHO (17 November, 20 December 2021, 28 December 2021, 11 January 2022, February 12, 2022 and March 2, 2022) and virtual meetings held on December 16, 2021, December 28, 2021, January 6, 2022, February 25, 2022 and the regional SEARO webinar held on February 10, 2022. During these exchanges, questions Specific issues have been raised by India as well as other Member States, e.g. China, Iran, Bangladesh, Syria, Ethiopia and Egypt regarding methodology and use of datasets unofficial,” the Ministry of Health and Family Welfare said in a response to the New York Times article titled “India Blocks WHO Effort to Make Global Covid Death Toll Public.” from April 16, 2022

The concern is specifically with how the statistical model projects estimates for a country of India’s geographic size and population and also fits in with other countries that have a smaller population. Such a one-size-fits-all approach and patterns that are true for smaller countries like Tunisia may not be applicable to India with a population of 1.3 billion. The WHO has not yet shared the confidence interval of the current statistical model between the different countries.

The model yields two very different sets of excess mortality estimates when using data from Tier I countries and when using unverified data from 18 Indian states. Such variation in estimates raises concerns about the validity and accuracy of such a modeling exercise.

India asserted that if the model is accurate and reliable, it should be authenticated by running it for all Tier I countries and if the outcome of such an exercise can be shared with all Member States.

The model assumes an inverse relationship between monthly temperature and monthly average deaths, which has no scientific basis for establishing such a particular empirical relationship. India is a country of continental proportions, climatic and seasonal conditions vary greatly from state to state and even within a state. Therefore, all states have widely varying seasonal patterns. Thus, estimating mortality at the national level based on these data from 18 states is not statistically proven.

The 2019 Global Health Estimates (GHE) on which the modeling for Tier II countries is based is itself an estimate. This modeling exercise appears to provide its own set of estimates based on another set of historical estimates, while ignoring data available with the country. It is unclear why GHE 2019 was used to estimate expected death figures for India, whereas for Tier 1 countries their own historical datasets were used when it was repeatedly pointed out that India has a robust data collection and management system. .

In order to calculate the distribution of deaths by age and sex for India, WHO determined standard patterns for age and sex for countries with reported data (61 countries) and then generalized them. to other countries (including India) that did not have such distribution in India. their mortality data. Based on this approach, the age and sex distribution of predicted deaths in India was extrapolated based on the age and sex distribution of deaths reported by four countries (Costa Rica, Israel, Paraguay and Tunisia).

Among the covariates used for the analysis, a binary measure of income was used instead of a more realistic scaled variable. Using a binary variable for such a large measurement can lend itself to magnifying the magnitude of the variable. The WHO reported that a combination of these variables was found to be the most accurate in predicting excess mortality for a sample of 90 countries and 18 months (January 2020-June 2021). The detailed justification of how the combination of these variables turns out to be the most accurate has not yet been provided by WHO.

The test positivity rate for Covid-19 in India has never been uniform across the country at any time. But this variation in covid-19 positivity rate within India was not taken into account for modeling purposes. Moreover, India has undertaken COVID-19 testing at a much faster rate than the WHO has advised. India has maintained molecular testing as the preferred testing methods and has only used Rapid Antigen for screening purposes. It remains unclear whether these factors were used in the model for India.

Lockdown involves many subjective approaches (such as closing schools, closing workplaces, canceling public events, etc.) to quantify. But, it is actually impossible to quantify various containment measures in this way for a country like India, as the stringency of such measures has varied greatly even between states and districts in India. Therefore, the approach followed in this process is highly questionable. Moreover, a subjective approach to quantify such measures will always involve a lot of biases which will surely not present the real situation. The WHO also agreed with the subjective approach of this measure. However, it is still in use.

Although India has expressed above and other similar concerns with WHO, a satisfactory response has not yet been received from WHO.

During interactions with the WHO, it was also pointed out that some fluctuations in the official reporting of COVID-19 data from some of the Tier I countries, including the United States, Germany, France, etc. challenged knowledge of the epidemiology of the disease. The additional inclusion of a country like Iraq which is experiencing a protracted complex emergency in Level I countries raises doubts about the WHO assessment in the categorization of countries into Level I/II and its affirmation on the quality of mortality reports from these countries.

“While India has remained open to collaboration with the WHO, as datasets like these will be useful from a policy-making perspective, India believes that further clarity on the methodology and clear evidence of its validity are essential for policy makers to feel confident in any use of this data,” the statement added.